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Strength Training as an Adjunct to the Maintenance of Muscle Mass in Patients with Head and Neck Cancer

2018· article· en· W2802503627 on OpenAlexvenueno aff
Adilson Domingos dos Reis Filho, Fernando Tadeu Trevisan Frajácomo, Roberto Carlos Vieira, Haracelli Christina Barbosa Alves Leite da Costa, James W. Navalta, Ramires Alsamir Tibana, Jonato Prestes, Fabrí­cio Azevedo Voltarelli

Bibliographic record

VenueJournal of Analytical Oncology · 2018
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineQuality of life (healthcare)Strength trainingPhysical therapyHead and neck cancerAerobic exerciseCancerDiseasePhysical medicine and rehabilitationHead and neckInterval trainingPhysical strengthInternal medicineOncologySurgeryNursing

Abstract

fetched live from OpenAlex

Head and neck cancer (HNC) is one of the most common types of the disease, particularly among men, and is characterized by a high incidence of death. Among the non-pharmacological factors that help in survival and improving quality of life is physical exercise, especially strength training. The purpose of this short communication was to briefly review the literature and present a training proposal for oncology patients with HNC. Evidence is provided that physical exercise, mainly short-term strength (HIIT [High-Intensity Interval Training]) and aerobic training, contributes to increased expectation and quality of life in cancer survivors. After reviewing the current state of literature, we conclude that strength training, by providing maintenance of muscle mass, improves the autonomy and quality of life of oncology patients with HNC.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score0.574

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.336
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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